A Comparison of YOLO Based Vehicle Detection Algorithms

Abstract

The use of vehicle object detection in intelligent video surveillance and vehicle-assisted driving has expanded as science and technology have advanced. Traditional car object detection algorithms have some limitations in their generalization capacity and recognition rate. The primary goal of this survey is to detect the vehicle, which forms managing crucial traffic data, including vehicle detection, vehicle count, and vehicle movement. This research compares modern object detectors that incorporate traffic situation estimations To determine which version of the YOLO algorithm is the best for detecting the vehicle explained here. Process of the YOLO algorithm the dataset is the first clustered using the clustering analysis approach, and the network structure is improved to increase the vehicle prediction capacity and the final numbers of output grids. In the second process, it maximizes both input image and dataset collection. This research suggests a better vehicle identification technique based on YOLO (You Only Look Once) to address this issue. Three versions of the YOLO (You Only Look Once) algorithm are evaluated to detect the vehicle.